The brief was simple: the company needed one place where employees could ask questions and get answers grounded in actual company documentation, not AI hallucinations dressed up as company policy. Four tools made the shortlist. Here is what I found after two months of real testing.
The contenders
Guru, Tettra, Notion AI connected to their existing Notion workspace, and a self-hosted option running on their own infrastructure. I am not going to rank them one through four because the right answer genuinely depends on the organization. What I can do is tell you what each one actually does well and where I ran into walls.
Guru
The structured Q&A approach works well for a knowledge base that someone is actively maintaining. The verification system, where subject matter experts are assigned to keep specific cards up to date, is genuinely good for organizations that can operationalize it. The AI search feels reliable because it is grounded in the cards rather than doing open-ended retrieval across unstructured documents.
The wall I ran into: this company had most of their knowledge in unstructured documents, not structured Q&A. Guru works best when someone has done the work of structuring knowledge into cards. If your knowledge base is a collection of Confluence pages, Google Docs, and Notion pages with varying levels of quality, the effort required to get to good Guru results is significant.
Tettra
Similar positioning to Guru. Better fit for teams that want a curated, expert-maintained knowledge base rather than one that ingests everything. The Slack integration is genuinely useful, employees can ask questions in Slack and get answers without leaving their workflow.
Same fundamental limitation: requires upfront curation effort that some teams will sustain and others will not. When the curation slips, the quality slips, and the AI starts surfacing outdated answers with the same confidence it surfaces current ones.
Notion AI on existing Notion workspace
The path of least resistance for this company because they were already in Notion and had significant documentation there. Setup was fast, the AI was immediately useful for the content that was in Notion, and the integration felt natural.
The problem was the same problem I see with every workspace-embedded AI: the AI was as good as the content in the workspace, which was inconsistent. Well-maintained pages gave good answers. Pages that had not been touched in a year gave bad answers with no indication of their staleness. The AI had no way to signal confidence differences based on document freshness.
The other issue was that Notion's permission model, while present, did not enforce cleanly at the AI retrieval layer. Queries from general employees were occasionally surfacing content from pages that had been shared broadly at some point but were not intended for general consumption.
The self-hosted option
This one required more setup than the others, around three days of configuration work from their internal engineer. The payoff was meaningful: full control over what got indexed, retrieval-layer access control that matched their existing permission structure, and inference running entirely on their own infrastructure.
For a company that handles customer data and has enterprise clients with security requirements, the self-hosted option was the only one that could answer "where does our data go when an employee uses the AI assistant" with a clean, simple answer. It stays here.
The tool they evaluated was PrivOS (https://privos.ai/), which packages the knowledge base, chat, and AI layer as a unified self-hosted deployment. The room-based access control model mapped reasonably well to how this company was already thinking about data access by department.
My actual recommendation
For this specific company, the self-hosted option was the right answer. The security requirements were real, the internal engineering capacity existed, and the control over data handling was worth the setup overhead.
For a different company, say one without meaningful security requirements, without an internal engineer who can handle the setup, and with content already well-organized in Notion, Notion AI would be the faster and more practical choice.
The evaluation question that matters most is: what is your actual requirement around where your data goes during AI processing? If the answer is "it needs to stay on our infrastructure," that narrows the field significantly and quickly. If the answer is "we just need good answers and we are not concerned about data residency," you have more options and can optimize for ease of setup and quality of results.
Top comments (0)